PCL methods can estimate not only the structural function, but also other causal quantities, such as Conditional Average Treatment Effect (CATE) [1]} and Average Treatment Effect on Treated (ATT) [2]}, [1]}. Beyond the causal context, [4]} uses a PCL method to conduct off-policy evaluation in a partially observable Markov decision process with discrete action-state spaces. Here, we present the application of PCL methods to bandit off-policy evaluation with continuous actions, and propose a method to leverage adaptive feature maps in this context.
